System and method for identifying data useful for valve diagnostics

ABSTRACT

Embodiments of systems and methods that can facilitate data collection for valve diagnostics. The systems can include a valve assembly with a valve and a sampling device that is configured to access a repository with a first buffer and a second buffer. During operation, the valve assembly is configured to read data representing operating variables for the valve into the first buffer. The valve assembly is also configure to determine a quality measure for a first sample set of data from the first buffer, the quality measure indicating the usefulness of the first sample set of data for predicting performance of the valve relative to a second sample set of data from the second buffer. In one embodiment, the valve assembly is further configured to read data from the first buffer into the second buffer in response to the quality measure indicating that the first sample set of data is relatively more useful than the second sample set of data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. Ser. No. 14/140,012, filed onDec. 24, 2013, and entitled “SYSTEM AND METHOD FOR IDENTIFYING DATAUSEFUL FOR VALVE DIAGNOSTICS.” The content of this application isincorporated herein by reference in its entirety.

BACKGROUND

The subject matter disclosed herein relates to valves and valvediagnostics with particular discussion below that describes techniquesto improve efficiency of data collection for diagnostic analysis of avalve assembly on a process line.

Industrial factories and like facilities operate process lines that mayinclude many varieties of flow controls. Examples of these flow controlsinclude pneumatic and electronic valve assemblies (also “controlvalves”) that regulate a flow of process fluid (e.g., gas and liquid).In conventional configurations, these valve assemblies have a number ofcomponents that work together to regulate flow of process fluid throughthe valve assembly. These components include a stem, a plug, a seat, andan actuator that couples with the stem to change the position of theplug relative to the seat. The components can also include variouslinkages and springs that ensure proper movement, e.g., of the stemand/or the plug. In some constructions, the valve assembly incorporatesa valve positioner with electrical and/or electro-pneumatic components.During operation, the valve positioner instructs the actuator to changethe position of the plug relative to the seat. Often, the valvepositioner issues the instructions in response to control signals from acontroller that is part of a process control system (also “distributedcontrol system” or “DCS”). The instructions are part of managementfunctions in the DCS that can, inter alia, cause the valve assemblies tooperate in a manner that achieves the process parameters set out for theprocess line.

Facilities and operators often allow techniques that collect data fromthe valve assemblies to diagnose issues that could be detrimental tooperation of the process lines. These techniques typically do notinterrupt operation of the valve assemblies. The data may include datathat relates to operative variables including setpoint, pressure,position, and like information. This data is available via the DCS, thevalve positioner, and/or other components in the facility. However,although the data that reflects the operating variables is helpful todiagnose problems with the valves, processes are meant to minimizevariations in operating variables to maintain stability andpredictability of the process output. The stability of the processrequires techniques to continuously collect data from the valveassemblies to increase the likelihood that the data collected willreveal observable movement in the components of valve assembly. Thismovement is critical for proper diagnosis of the device using manyonline diagnostics and related predictive maintenance techniques.Unfortunately, the vast number of valve assemblies in use in thefacility, as well as limits on bandwidth on the systems/networks togather data, can frustrate the process of data collection. Theselimitations can prevent diagnostic techniques to capture enough data toidentify movement or other activities of the valve assemblies, let aloneto observe problems with one or more valves assemblies on the processline.

BRIEF DESCRIPTION OF THE INVENTION

This disclosure describes improvements that can identify data bestsuited for use in diagnostic programs and for related processing todetermine operations of the valve assemblies on the process line. Theseimprovements can implement data processing on-board the valve assembly,thus reducing reliance of the valve diagnostic system on the controlsystem network for data acquisition and sampling. In this way, thediagnostic system receives data that is most likely to result inanalysis at the diagnostics programs that can effectively predict theonset of problems and/or issues on the valve assembly. Moreover,on-board data capture, analysis, and processing is also advantageous toanalyze variables that can change rapidly. This feature can helpunderstand dynamic behavior of the valve assembly.

Examples of these diagnostics programs can use the data to determine oneor more performance indicators that are useful to gauge and,importantly, predict future performance of the valve 102. Examples ofthese performance indicators include friction, spring range, lag, stickslip, and like parameters that can, in one example, be mathematicallycalculated from the data representing the operating variables discussedherein. For several examples of such mathematical calculations,reference can be had to U.S. Pat. No. 7,089,086 to Schoonover andcommonly assigned to the Assignee designated in the present application.The content of this patent is incorporated by reference in its entiretyherein.

As set forth more below, this disclosure presents embodiments of systemsand methods that can facilitate data collection for valve diagnostics.The systems can include a valve assembly with a valve and a samplingdevice that is configured to access a repository with a first buffer anda second buffer. During operation, the sampling device is configured toread data representing operating variables for the valve into the firstbuffer. The valve assembly is further configure to determine a qualitymeasure for a first sample set of data from the first buffer, thequality measure indicating the usefulness of the first sample set ofdata for predicting performance of the valve relative to a second sampleset of data from the second buffer. In one embodiment, the valveassembly is further configured to read data from the first buffer intothe second buffer in response to the quality measure indicating that thefirst sample set of data is relatively more useful than the secondsample set of data.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference is now made briefly to the accompanying drawings, in which:

FIG. 1 depicts a schematic diagram of an exemplary embodiment of asystem that is configured to can facilitate data collection for use invalve diagnostics;

FIG. 2 depicts a flow diagram of an exemplary embodiment of a method toidentify the data that is useful for valve diagnostics;

FIG. 3 depicts a flow diagram of the an example of the method of FIG. 2with details of steps to evaluate the usefulness of data for valvediagnostics;

FIG. 4 depicts a schematic diagram of an exemplary embodiment of asystem that is configured to can facilitate data collection for use invalve diagnostics as part of a process system having a process line;

FIG. 5 depicts a perspective view of an example of a control valveassembly; and

FIG. 6 depicts a perspective view of an example of a valve positioner inexploded form.

Where applicable like reference characters designate identical orcorresponding components and units throughout the several views, whichare not to scale unless otherwise indicated.

DETAILED DESCRIPTION

FIG. 1 depicts a schematic diagram of an exemplary embodiment of asystem 100 that can facilitate data collection for valve diagnostics.The system 100 includes a valve 102 with components (e.g., an actuator,a valve stem, a plug, a seat, etc.) that are configured to regulate theflow of a process fluid F. The system 100 also includes a samplingdevice 104 that couples with the valve 102 and has access to one or morebuffers (e.g., a first 106 and a second buffer 108). The sampling device104 may include a processor 110 and memory 112 with executableinstructions 114 stored thereon. Examples of the executable instruction114 may include all or part of a software program, firmware, and/orrelated listing of operative instructions that can cause the samplingdevice 104 to perform one or more actions as described herein. In oneembodiment, the valve 102, and/or the sampling device 104, and/or thebuffers 106, 108 may be part of a valve assembly 116 with a valvepositioner 118 that is configured to control operation of the valve 102.The valve positioner 118 may couple with and/or, in the constructionshow in FIG. 1, incorporates all or part of the sampling device 104 andthe buffers 106, 108. This disclosure also contemplates configurationsof the repositories and/or buffers 106, 108 in which one or more of therepositories are located remote from the valve assembly 116.

The system 100 can gather and interrogate data to identify data havingparticular utility to analysis of the operation of the valve 102 andother related valve diagnostics. The sampling device 104 may, forexample, acquire data that describes operation of one or more of thecomponents of the valve 102. This data may represent one or moreoperating variables (e.g., setpoint, position, actuator pressure, etc.)for the valve 102. In one implementation, the system 100 can process thedata to identify data that is best suited for use in diagnosticprocesses. These processes may occur on or at the sampling device 104,which can embody an “on-board” operative configuration to collect,store, and process diagnostic data locally at the valve assembly 116.

As set forth more below, the system 100 can deliver the identified, ormore useful, data to any suitably configured diagnostic program.However, unlike conventional diagnostic systems that provide onlyperiodic samples of data from the valve 102 for analysis, the proposedconfiguration of the system 100 can continuously (and periodically inmanner that approximates continuous sampling, as desired) sample datafrom the valve 102. This configuration can deliver only the mostpertinent data for use in the diagnostic processes. This feature of thesystem 100 provides data to the diagnostic programs that is more likelyto result in favorable analysis of the performance indicators toeffectively diagnosis and/or predict operation of the valve 102.Moreover, moving the functions for collecting and interrogating the datato the system 100 avoids the bandwidth problems that plague conventionaldata sampling and diagnostic techniques.

FIG. 2 depicts a flow diagram of an exemplary embodiment of a method 200to identify the data that is useful for valve diagnostics. The method200 includes, at step 202, reading data representing one or moreoperating variables for the valve into the first buffer. The method 200also includes, at step 204, determining a quality measure for a firstsample set of data from the first buffer and, at step 206, using thequality measure to identify whether the first sample set of data is moreuseful than the second sample set of data. If the first sample set ofdata is more useful that the second sample set of data, then the method200 includes, at step 208, reading data from the first buffer into thesecond buffer. On the other hand, if the first sample set of data is notmore useful than the second sample set of data, then the method 200 cancontinue, at step 200, to read (or acquire) more data for furtherprocessing and evaluation in accordance with the embodiments set forthherein.

The step of reading data into the first buffer (e.g., at step 202) caninstruct the sampling device 104 to direct data representing theoperating variables for the valve 102 to certain repositories (e.g.,memory). This data may originate from sensors and other devices (e.g.,the actuator, a regulator, a pressure/current converter, etc.), one ormore of which provides information about the operation of the valve 102.As shown in FIG. 1, these repositories may include one of the buffers106, 108. Examples of the repositories can utilize a circular datastructure that has a fixed size and/or capacity. This structure allowsthe sampling device 104 to continuously acquire data from the valve 102,in effect generating a constant stream of data that is stored, e.g., inthe first buffer 106. During operation, when the capacity of the firstbuffer 106 is reached, the system 100 is configured to read data thatthe sampling device 104 acquires over previously stored data in thefirst buffer 106.

The system 100 can read (also “store”) and/or arrange the data in therepository (e.g., the first buffer 106 and the second buffer 108) as atable, database, and/or like configuration. In one example, the dataincludes one or more datasets that group one or more measured data taken(and/or collected and/or acquired) by the sampling device 104 atrelatively the same time. Examples of the sample set (e.g., the firstsample set) identify a time-sequenced collection of datasets. Thiscollection can include a plurality of datasets acquired over time

Table 1 below provides an exemplary arrangement of data that makes up asample set. During operation, the sampling device 104 can read this datainto the first buffer 106. In other configurations, the sample set maycomprise only part of the data that the sampling device 104 reads intothe first buffer 106. The table lists the data in datasets, which is oneway in which the data is collected and organized for use in evaluatingthe performance of a valve 102. In the example of Table 1 below, thesample set includes ten datasets, each dataset comprising a value forone or more operating variables, namely, setpoint (S), position (P), andactuator pressure (AP). Notably, this disclosure contemplates that thereare a wide range of other operating variables that may be included ineach dataset in accordance with the concepts disclosed herein.

TABLE 1 Dataset S P AP 1 S1 P1 AP1 2 S2 P2 AP2 3 S3 P3 AP3 4 S4 P4 AP4 5S5 P5 AP5 6 S6 P6 AP6 7 S7 P7 AP7 8 S7 P8 AP8 9 S9 P9 AP9 10 S10 P10AP10

The amount of data in the first sample set can be defined by a samplingtime interval and/or other chronological factor. For the system 100 ofFIG. 1, the sampling time interval can provide a relative measure of thetime between the first dataset (e.g., dataset 1 of Table 1) and the lastdataset (e.g., dataset 10 of Table 1) as the sampling device 104 readsdata representing the operating variables from the first valve 102 intothe first buffer 106.

The step of determining the quality measure (e.g., at step 204) isuseful to differentiate the first sample set from a second sample set.In an embodiment of the system 100 of FIG. 1, the second sample setincludes data that the system 100 identifies as best suited for use indiagnostic processes. This embodiment can store the second sample set inthe second buffer 108, which effectively distinguishes the data in thesecond sample set from other data that the sampling device 104 acquiresand stores in the first buffer 106. In one example, the second sampleset includes data the sampling device 104 acquires prior to (and/orchronologically before) the first sample set. Thus, in chronologicalterms, the first sample set contains current data (e.g., in the firstbuffer 106) and the second sample set contains past data (e.g., in thesecond buffer 108).

The quality measure can indicate the usefulness of the first sample setof data relative to the second sample set of data. The system 100 ofFIG. 1 can use the quality measure (e.g., at step 206) to identifywhether current data the sampling device 104 acquires from the valve 102is better suited that the prior data in the second buffer 108 tocalculate certain performance indicators that predict performance of thevalve 102. As noted above, unlike conventional data processing, thesystem 100 acts as an intermediary to screen data before the data iscommunicated to a host device and/or other diagnostic processing devicefor use in diagnostic programs. This feature alleviates some of thebandwidth issues that prevail on may data networks in processfacilities, thus presenting only data that is most likely, if at all, toidentify issues and/or problems during further processing and analysis.

The step of reading data from the first buffer to the second buffer(e.g., at step 208) can modify the contents of the repositories toreflect the quality measure associated with the first sample set ofdata. In the system 100 of FIG. 1, for example, if the quality measureindicates that the first sample set is more useful that the secondsample set, the system 100 may read all or part of the data in the firstsample set from the first buffer 106 into the second buffer 108. Thisaction may replace, or overwrite, the second sample set of data in thesecond buffer 108 with the data of the first sample set. In this way,the system 100 maintains the second buffer 108 with data that is mostuseful to calculate the performance indicators that predict performanceof the valve. As discussed more below, the determination of usefulnessof data may utilize the first value and the second value, wherein in oneimplementation, the quality measure indicates that the first value isdifferent from the second value.

Other embodiments may include a plurality of buffers (e.g., a thirdbuffer, a fourth buffer . . . n buffers, wherein n identifies the numberof buffers available to receive data). Each of the plurality of buffersmay contain a sample set of data (e.g., a third sample set, a fourthsample set, . . . n sample set, wherein n identifies the number ofsamples sets of data). In these embodiments, the system 100 can readdata from the first buffer 106 into one of the plurality of buffersincluding the third buffer, when, for example, the quality measure ofthe indicates the first sample set is more useful than the second sampleset, third sample set, the forth sample set, etc. This configuration canallow the system to maintain a plurality of sample sets (e.g., n samplesets) of varying degrees of usefulness by the diagnostic programs.

FIG. 3 depicts a flow diagram of an example of the method 200 thatprovides additional details as to the determination of the qualitymeasure to identify the data that is useful for valve diagnostics. InFIG. 3, the exemplary method 200 includes, at step 210, calculating afirst value of a diagnostic statistic using the first sample set. Themethod 200 also includes, at step 212, comparing the first value to asecond value of the diagnostic statistic for the second sample set ofdata to determine the quality measure and, at step 214, using therelative measure (and/or position) of the first value relative to thesecond value to identify whether the first sample set of data is moreuseful than the second sample set of data.

The step of calculating the first value (e.g., at step 210) can use thedata in the first sample set to determine one or more metrics thatdescribe the performance of the valve 102. These performance metricsquantify certain aspects of the data. These aspects can includevariance, deviation, trends, and other quantitative and/or statisticalrelationships that might exist between the data found the first sampleset. In the system 100 of FIG. 1, the system 100 may select and useperformance metrics that identify variance in setpoint S and/or or valveposition P across all of the datasets in the first sample set. Thesystem 100 may also quantify (or aggregate) the number of times thevalve position P indicates a change of direction in the first sample setor, in one example, when a change of direction occurs after movementgreater than a set amount in the first sample set. In other examples,the system 100 may quantify a maximum deviation in one of the operatingvalues across the first sample set, e.g., as the maximum valve positionP minus the minimum valve position P in the first sample set and/or asthe maximum setpoint S minus the minimum setpoint S in the first sampleset.

Embodiments of the method 200 may further combine and/or aggregate theperformance metrics together to arrive at a single value for thediagnostic statistic. This functionality can provide, for example, aweighted average of the performance metrics. In one example, thisweighted average may be calculated according to Equation (1) below:D _(S) =W ₁ P ₁ +W ₂ P ₂ + . . . W _(i) P _(i,)  Equation (1)

-   -   wherein D_(S) is the diagnostic statistic, P_(i) is the        performance metric, and W_(i) is a weighting factor that is        assigned and/or prescribed to each of the performance metrics.        This weighting factor provides a measure of the importance of        the performance metrics relative to one another. Using the        discussion above as an example, the variance of the setpoint S        may have be weighted relatively more importantly than the        deviation of the valve position P.

The step of comparing the first value to a second value for thediagnostic statistic (e.g., at step 212) can identify the usefulness ofthe first sample set of data. In one example, the system 100 candetermine the relationship (e.g., at step 214) between the first valueand the second value, i.e., whether the first value is different fromthe second value, the first value is not equal to the second value, thefirst value is greater than the second value, the first value is lessthan the second value, the first value is equal to the second value. Thesystem 100 can associate this relationship between the first value andthe second value to the quality measure, thereby identifying therelative usefulness of the first sample set of data relative to thesecond sample set of data for performing diagnostic statistics. As notedabove, the relationships may indicate that the first sample set is moreuseful for valve diagnostics than the second set, and vice versa.

Embodiments of the method 200 may also include one or more steps forretrieving the second value from the repository (e.g., the second buffer108) and/or for calculating the second value from the second sample setof data as set forth herein. The method 200 may likewise include one ormore steps for storing and/or retrieving the second value (and the firstvalue) from the repository.

FIG. 4 illustrates an exemplary embodiment of a system 300 as part of aprocess system 320 found, commonly, at industrial plants, facilities,and factories. The process system 320 includes a network 322 that maydeploy various wired and wireless constructions to facilitate theexchange of data and information In many facilities, for example, theconstructions utilize HART®, FOUNDATION® Fieldbus, and likecommunication protocols among components of the process system 320.These components may include a process controller 324, a managementserver 326, and a process line 328 with one or more process devices(e.g., a first device 330, a second device 332, and a third device 334).As contemplated herein, one or more of the process device 330, 332, 334can embody the structure of the valve assembly 300 to modulate flow ofprocess fluids in the process line 328. The process system 320 may alsoinclude one or more external servers (e.g., a first external server 336)that are useful for data collection and storage and other peripheralfunctions. The process system 320 may further include one or moreterminals (e.g., a first terminal 338). Examples of the terminal 338 caninclude a variety of computing devices (e.g., personal computers,workstations, laptop computers, tablet computers, smartphones, etc.)that an end user can utilize to interface with the process controller324, the servers 326, 336, and/or the process devices 330, 332, 334.

The process controller 324 can be part of a distributed control system(“DCS”) that issues commands over the network 322 to the process devices330, 332, 332. For control valve assemblies, these commands can instructthe valve positioner to operate the actuator to modulate flow throughthe valve assembly. The management server 326 (and/or the sever 336 andterminal 338) can communicate with process devices 330, 332, 334 throughthe DCS or, in one example, directly via the network 322. Thisconfiguration allows the management server 326 to collect and processdata to provide, among other things, overall guidance as to theoperation of the process line 328 (and, in certain configurations, theoperation of components of the system 320 and the process facility ingeneral).

Components of the process system 320 may generate signals to the system300 to solicit data for use in diagnostic processes. These signals may,for example, instruct the system 300 to communicate data most useful todetermine the performance metrics discussed herein. In oneimplementation, the system 300 can generate an output that comprises allor part of the data that is stored in the second buffer 308, which asnoted herein may include data identified by the system 300 as being mostuseful for valve diagnostics. The output may, in other examples, includeother data, e.g., values for the quality measure, the first value of thediagnostic statistic, and/or the second value of the diagnosticstatistic, which the components of the process system 320 may requestfrom time-to-time, e.g., via the signals generated to the system 300.

FIGS. 5 and 6 illustrate an exemplary embodiment of a system in the formof a control valve assembly 440. In FIG. 5, the control valve 440includes a valve positioner 442, an actuator 444, and a fluid coupling446 with a body 448 that has a first inlet/outlet 450 and a secondinlet/outlet 452. This structure is typical of devices that can modulatea flow of working fluid between the inlet/outlets 450, 452.

FIG. 6 depicts an example of the valve positioner 442 in exploded form.As shown in this diagram, the valve positioner 442 has a plurality ofvalve components (e.g., a converter component 454, a relay component456, a processing component 458). The valve components 454, 456, 458work in combination to maintain the position of a valve disposed in thebody 448 (FIG. 5) to modulate fluid flow across the inlet/outlets 450,452 (FIG. 5). In one example, the processing component 458 can includeoperative hardware with one or more processing units (e.g., a firstprocessing unit 460, a second processing unit 462, a third processingunit 464, and a fourth processing unit 466).

Examples of the processing component 458 manage operation of the valvecomponents 454, 456 to regulate flow of working fluid across the valveassembly 440 (FIG. 5). These examples can comprise one or more discretecomponents (e.g., resistors, transistors, capacitors, etc.) includingprocessing units 460, 462, 464, 466 that reside on one or moresubstrates (e.g., a printed circuit board). These components may includeone or more processors (e.g., an ASIC, FPGA, etc.) that can executeexecutable instructions in the form of software, computer programs, andfirmware. These executable instructions can be stored on memory. In oneembodiment, the processing component 458 can include one or moreprogrammable switches, inputs that couple with sensors for positionfeedback, a proportional-integral-derivative (PID) controller, a display(e.g., an LCD display), and like components that facilitate use andoperation of the control valve assembly 440 (FIG. 5).

In view of the foregoing, this disclosure contemplates configurations ofa valve assembly (and/or system and/or valve positioner) that areconfigured to perform steps and operations to process data. These stepscan determine a quality measure for a first sample set of data relativeto a second sample set of data and generate an output. The steps an alsoincorporate one or more of the processing steps recited herein. Examplesof the output can convey information that reflects this quality measure.As noted herein, the output may comprise data (e.g., data from the firstsample set), the quality measure, the first value, the second value, andother data, as desired). In one example, the output is configured readdata into a repository, e.g., having a first buffer and a second buffer.

In one embodiment, a valve assembly (and/or system and/or valvepositioner) comprises a sampling device comprising a processor havingaccess to a memory with executable instructions stored thereon andconfigured to be executed by the processor, wherein the executableinstructions comprise instructions for determining a quality measure fora first sample set of data, the quality measure indicating theusefulness of the first sample set of data relative to a second sampleset of data; and generating an output that reflects the quality measure,wherein in one example the output comprises data from the first sampleset in response to the quality measure indicating a first value for adiagnostic statistic for the first sample set of data is different froma second value for the diagnostic statistic for the second sample set ofdata. This response can reflect that the first sample set of data isrelatively more useful than the second sample set of data.

Accordingly, a technical effect of embodiments of the methods, andsystems and devices implementing these methods, is to identify data thatis most useful for valve diagnostics and related processing of valveassemblies on a process line. The methods determine a quality measurethat distinguishes a first sample set of data from a second sample setof data. This quality measure can help determine whether data in thefirst sample set is better suited for use in diagnostic programs thatcan analyze performance of valve assembles.

One or more of the steps of the methods (e.g., methods 200) can be codedas one or more executable instructions (e.g., hardware, firmware,software, software programs, etc.). These executable instructions can bepart of a computer-implemented method and/or program, which can beexecuted by a processor and/or processing device. The processor may bepart a component that is adapted to execute these executableinstructions, as well as to process inputs and to generate outputs, asset forth herein. For example, the software can run on the valveassembly, valve positioner, and process system and/or as software,application, or other aggregation of executable instructions on aseparate computer, tablet, lap top, smart phone, and like computingdevice.

Examples of a processor can integrate into the process line and/orreside remote from the process line as a standalone computing device,network, and like computing arrangement. The memory and the processorcan include hardware that incorporates with other hardware (e.g.,circuitry) to form a unitary and/or monolithic unit devised to executecomputer programs and/or executable instructions (e.g., in the form offirmware and software). In other examples, these devices integrate, inwhole or in part, with components of the valve assemblies, samplingdevices, and other components contemplated herein.

Exemplary circuits of this type include discrete elements such asresistors, transistors, diodes, switches, and capacitors. Examples of aprocessor include microprocessors and other logic devices such as fieldprogrammable gate arrays (“FPGAs”) and application specific integratedcircuits (“ASICs”). Memory includes volatile and non-volatile memory andcan store executable instructions in the form of and/or includingsoftware (or firmware) instructions and configuration settings. Althoughall of the discrete elements, circuits, and devices functionindividually in a manner that is generally understood by those artisansthat have ordinary skill in the electrical arts, it is their combinationand integration into functional electrical groups and circuits thatgenerally provide for the concepts that are disclosed and describedherein.

Moreover, as will be appreciated by one skilled in the art, aspects ofthe present disclosure may be embodied as a system, method or computerprogram product. The embodiments can take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” For computerprogram products, the executable instructions may reside on one or morecomputer readable medium(s), for example, a non-transitory computerreadable medium having computer readable program code embodied thereon.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languageand conventional procedural programming languages. Program code embodiedon a computer readable medium may be transmitted using any appropriatemedium, including but not limited to wireless, wireline, optical fibercable, RF, etc., or any suitable combination of the foregoing.

As used herein, an element or function recited in the singular andproceeded with the word “a” or “an” should be understood as notexcluding plural said elements or functions, unless such exclusion isexplicitly recited. Furthermore, references to “one embodiment” of theclaimed invention should not be interpreted as excluding the existenceof additional embodiments that also incorporate the recited features.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal language of the claims

What is claimed is:
 1. A valve positioner, comprising: a relay; a signalconverter; and a processing component comprising a processor, memorycoupled with the processor, and executable instructions stored in thememory and configured to be executed by the processor, the executableinstructions for steps that, when executed by the processor, configurethe processor for: reading datasets that quantify values for setpoint,position, and actuator pressure for a valve into a first buffer;calculating a first value of a diagnostic statistic using a first sampleset of data from the first buffer, the first sample set of data definedby elapse of a sampling time interval that measures time between a firstdataset and a last dataset; comparing the first value to a second valueof the diagnostic statistic for a second sample set of data from asecond buffer, the second set of data acquired chronologically beforethe first sample set, the first value and the second value of thediagnostic statistic quantifying performance of the valve as variance inone or more of the setpoint, position, and actuator pressure across alldatasets; using a relative position of the first value relative to thesecond value to assign a quality measure; reading the first sample setof data from the first buffer into the second buffer when the qualitymeasure distinguishes the first sample set of data from the secondsample set of data because the first value is different from the secondvalue and the first sample set of data shows observable movement of aplug on the valve; calculate a performance indicator from the data inthe second buffer, the performance indicator relating the observablemovement to onset of problematic operation of the valve; and generatingan output with instructions to operate the valve in response to a signalfrom a host device, the instruction reflecting the performance indicatorto cause the valve to operate to maintain stability of a process thatuses the valve.
 2. The valve positioner of claim 1, wherein theexecutable instructions comprises instructions for receiving a signaland generating an output communicating data from the second buffer inresponse to the signal.
 3. The valve positioner of claim 2, wherein thedata comprises one or more of the first value and the second value. 4.The valve positioner of claim 2, wherein the data comprises data fromone or more of the first buffer and the second buffer.
 5. The valvepositioner of claim 2, wherein the executable instructions compriseinstructions for clearing the second buffer of data and resetting thesecond value in response to the signal.
 6. The valve positioner of claim1, further comprising a repository comprising the first buffer and thesecond buffer.
 7. The valve positioner of claim 1, wherein thediagnostic statistic comprises a weighted average of performance metricsfor the valve.
 8. A method, comprising: operating a valve assembly tomaintain stability of a process that uses the valve assembly by: readingdatasets that quantify values for setpoint, position, and actuatorpressure for a valve into a first buffer; calculating a first value of adiagnostic statistic using a first sample set of data from the firstbuffer, the first sample set of data defined by elapse of a samplingtime interval that measures time between a first dataset and a lastdataset; comparing the first value to a second value of the diagnosticstatistic for a second sample set of data from a second buffer, thesecond set of data acquired chronologically before the first sample set,the first value and the second value of the diagnostic statisticquantifying performance of the valve as variance in one or more of thesetpoint, position, and actuator pressure across all datasets; using arelative position of the first value relative to the second value toassign a quality measure; reading the first sample set of data from thefirst buffer into the second buffer when the quality measuredistinguishes the first sample set of data from the second sample set ofdata because the first value is different from the second value and thefirst sample set of data shows observable movement of a component on thevalve assembly; calculating a performance indicator from the data in thesecond buffer, the performance indicator relating the observablemovement to onset of problematic operation of the valve assembly;transmitting data from the second buffer that reflects the performanceindicator; receiving a signal in response to the data; and generating anoutput with instructions to operate the valve in response to the signal.9. The method of claim 8, wherein the performance indicator correspondswith friction of the component.
 10. The method of claim 8, wherein theperformance indicator corresponds to stick slip of the component. 11.The method of claim 8, wherein the performance indicator corresponds toa spring range for the component.
 12. The method of claim 8, wherein thediagnostic statistic reflects a weighted average of performance metricsfor the valve.
 13. The method of claim 12, wherein the weighted averageincludes a first performance metric, a second performance metric that isdifferent from the first performance metric, and a plurality ofweighting factors comprising a first weighting factor and a secondweighting factor having values that quantify the importance of,respectively, the first performance metric and the second performancemetrics relative to one another.
 14. The method of claim 12, wherein theperformance metrics identify variance in setpoint.
 15. The method ofclaim 12, wherein the performance metrics identify variance in valveposition.
 16. The method of claim 12, wherein the performance metricsidentify a number of times valve position indicates a change ofdirection.
 17. The method of claim 12, wherein the performance metricquantifies a maximum deviation in one of setpoint, position, or actuatorpressure.
 18. A method, comprising: using a valve assembly to select andtransmit data to a host device by: creating first samples of data forsetpoint, position, and actuator pressure, the first samples defined byelapse of a sampling time interval that measures time between a firstdataset and a last dataset; comparing the first samples topreviously-acquired samples of data for setpoint, position, and actuatorpressure to identify observable movement of a component on the valveassembly; reading the first samples from a first buffer into a secondbuffer when a quality measure distinguishes the first sample set of datafrom the second sample set of data because the first value is differentfrom the second value and the first sample set of data shows observablemovement of the component occurs on the valve assembly; using the datain the second buffer to calculate a performance indicator in response toobservable movement, the performance indicator relating the observablemovement to onset of problematic operation of the valve assembly; usingthe host device to process the data from the valve assembly in adiagnostic program to generate and transmit a control signal to thevalve assembly that instructs operation of the valve assembly tomaintain stability of a process; receiving the control signal at thevalve assembly; and generating an output at the valve assembly thatreflects the instructions.
 19. The method of claim 18, wherein theperformance indicator corresponds with friction of the component. 20.The method of claim 18, wherein the performance indicator correspondswith stick slip of the component.